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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationWed, 03 Dec 2008 03:17:26 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/03/t1228300115wz13st5k2864wn3.htm/, Retrieved Fri, 17 May 2024 15:49:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28602, Retrieved Fri, 17 May 2024 15:49:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [] [2008-12-03 10:17:26] [ed75e673b8609ce7f7795f94157397be] [Current]
Feedback Forum
2008-12-06 12:54:39 [Nicolaj Wuyts] [reply] 
Deze vraag is correct beantwoord door de student
2008-12-07 19:22:33 [Jasmine Hendrikx] [reply] 
Evaluatie Q7:
De crosscorrelatiefunctie is goed berekend. De conclusie is redelijk goed. Er wordt gezegd dat men met de Cross correlation function wil kijken om men Y kan voorspellen aan de hand van het verleden van X. Dit is correct, maar je zou dit kunnen aanvullen door te zeggen dat je gaat kijken of het verleden, de huidige waarden of de toekomst van Xt kan helpen om Yt te voorspellen. De cross correlation function geeft dus de correlatie weer tussen verschillende variabelen (Yt en Xt, tussen Yt en Xt-1, tussen Yt en Xt-2, tussen Yt en Xt+1) . Er is dus duidelijk een verschil met de autocorrelatie. Bij autocorrelatie ga je de correlatie berekenen tussen Xt en Xt-1, tussen Xt en Xt-2,.. Autocorrelatie is dus de mate waarin Xt voorspeld kan worden op basis van zijn eigen verleden.
Je krijgt als output onder andere een tabel met allemaal correlatiecoëfficiënten. De laatste kolom geeft de correlatie weer tussen Yt en Xt+k.
X t+k is dus verschoven in de tijd. Het getal in de eerste kolom is k. De correlatie tussen Yt en Xt-11 is bijvoorbeeld gelijk aan 0.11. Wanneer k=0, betekent dit dat je de correlatie zult krijgen tussen Yt en Xt zonder verschuiving in de tijd. De eerste reeks coëfficiënten (k loopt dan van -14 tot 0), vertellen iets over de correlatie tussen het verleden van Xt en Yt. Je kijkt of Xt een leading indicator is of niet, of dat Xt een indicator is die je op voorhand informatie geeft over het verloop van Yt. Bij de tweede reeks coëfficiënten wordt k positief, je gaat hier dan kijken naar het verband tussen de toekomst van Xt en de huidige waarde van Yt.
De 2 stippellijnen in de grafiek geven het betrouwbaarheidsinterval weer. We zien inderdaad dat veel verticale lijntjes significant verschillend zijn van 0. Je zou dus kunnen zeggen dat zowel het verleden als de toekomst van Xt toelaat om Yt te voorspellen.
2008-12-08 19:34:47 [Erik Geysen] [reply] 
correcte oplossing + uitleg!

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101,2
100,5
98
106,6
90,1
96,9
125,9
112
100
123,9
79,8
83,4
113,6
112,9
104
109,9
99
106,3
128,9
111,1
102,9
130
87
87,5
117,6
103,4
110,8
112,6
102,5
112,4
135,6
105,1
127,7
137
91
90,5
122,4
123,3
124,3
120
118,1
119
142,7
123,6
129,6
151,6
110,4
99,2
130,5
136,2
129,7
128
121,6
135,8
143,8
147,5
136,2
156,6
123,3
104,5
143,6
Dataseries Y:
Download CSV
Histogram 
6.8
6.9
6.8
6.2
6.2
6.6
6.8
7.1
7.3
7.2
7
7
7
7.3
7.5
7.2
7.7
8
7.9
8
8
7.9
7.9
7.9
8.1
8.1
8.2
8.1
8.3
8.5
8.6
8.7
8.7
8.5
8.4
8.5
8.8
8.7
8.6
8
8.1
8.2
8.6
8.6
8.5
8.3
8.2
8.7
9.3
9.3
8.8
7.5
7.2
7.5
8.3
8.8
8.9
8.6
8.4
8.4
8.4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28602&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28602&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28602&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-14-0.071089448835987
-13-0.0275995718351819
-120.0680426432342766
-110.114386153824766
-100.0672312232815759
-90.00389134603984280
-80.0613196049597313
-70.137537258828515
-60.240952825940249
-50.360889483595106
-40.339051373346426
-30.307483344687382
-20.340994323029306
-10.395179275812406
00.487775892290028
10.479774929789653
20.345939882924494
30.243516750262989
40.311251723654423
50.348366433385266
60.396887473842276
70.525568053963978
80.477720202250854
90.372432566186509
100.423845592215471
110.397144244179591
120.393116711114787
130.399650329530564
140.304131784222107

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & -0.071089448835987 \tabularnewline
-13 & -0.0275995718351819 \tabularnewline
-12 & 0.0680426432342766 \tabularnewline
-11 & 0.114386153824766 \tabularnewline
-10 & 0.0672312232815759 \tabularnewline
-9 & 0.00389134603984280 \tabularnewline
-8 & 0.0613196049597313 \tabularnewline
-7 & 0.137537258828515 \tabularnewline
-6 & 0.240952825940249 \tabularnewline
-5 & 0.360889483595106 \tabularnewline
-4 & 0.339051373346426 \tabularnewline
-3 & 0.307483344687382 \tabularnewline
-2 & 0.340994323029306 \tabularnewline
-1 & 0.395179275812406 \tabularnewline
0 & 0.487775892290028 \tabularnewline
1 & 0.479774929789653 \tabularnewline
2 & 0.345939882924494 \tabularnewline
3 & 0.243516750262989 \tabularnewline
4 & 0.311251723654423 \tabularnewline
5 & 0.348366433385266 \tabularnewline
6 & 0.396887473842276 \tabularnewline
7 & 0.525568053963978 \tabularnewline
8 & 0.477720202250854 \tabularnewline
9 & 0.372432566186509 \tabularnewline
10 & 0.423845592215471 \tabularnewline
11 & 0.397144244179591 \tabularnewline
12 & 0.393116711114787 \tabularnewline
13 & 0.399650329530564 \tabularnewline
14 & 0.304131784222107 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28602&T=1

[TABLE]
[ROW][C]Cross Correlation Function[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of X series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of X series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-14[/C][C]-0.071089448835987[/C][/ROW]
[ROW][C]-13[/C][C]-0.0275995718351819[/C][/ROW]
[ROW][C]-12[/C][C]0.0680426432342766[/C][/ROW]
[ROW][C]-11[/C][C]0.114386153824766[/C][/ROW]
[ROW][C]-10[/C][C]0.0672312232815759[/C][/ROW]
[ROW][C]-9[/C][C]0.00389134603984280[/C][/ROW]
[ROW][C]-8[/C][C]0.0613196049597313[/C][/ROW]
[ROW][C]-7[/C][C]0.137537258828515[/C][/ROW]
[ROW][C]-6[/C][C]0.240952825940249[/C][/ROW]
[ROW][C]-5[/C][C]0.360889483595106[/C][/ROW]
[ROW][C]-4[/C][C]0.339051373346426[/C][/ROW]
[ROW][C]-3[/C][C]0.307483344687382[/C][/ROW]
[ROW][C]-2[/C][C]0.340994323029306[/C][/ROW]
[ROW][C]-1[/C][C]0.395179275812406[/C][/ROW]
[ROW][C]0[/C][C]0.487775892290028[/C][/ROW]
[ROW][C]1[/C][C]0.479774929789653[/C][/ROW]
[ROW][C]2[/C][C]0.345939882924494[/C][/ROW]
[ROW][C]3[/C][C]0.243516750262989[/C][/ROW]
[ROW][C]4[/C][C]0.311251723654423[/C][/ROW]
[ROW][C]5[/C][C]0.348366433385266[/C][/ROW]
[ROW][C]6[/C][C]0.396887473842276[/C][/ROW]
[ROW][C]7[/C][C]0.525568053963978[/C][/ROW]
[ROW][C]8[/C][C]0.477720202250854[/C][/ROW]
[ROW][C]9[/C][C]0.372432566186509[/C][/ROW]
[ROW][C]10[/C][C]0.423845592215471[/C][/ROW]
[ROW][C]11[/C][C]0.397144244179591[/C][/ROW]
[ROW][C]12[/C][C]0.393116711114787[/C][/ROW]
[ROW][C]13[/C][C]0.399650329530564[/C][/ROW]
[ROW][C]14[/C][C]0.304131784222107[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28602&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28602&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-14-0.071089448835987
-13-0.0275995718351819
-120.0680426432342766
-110.114386153824766
-100.0672312232815759
-90.00389134603984280
-80.0613196049597313
-70.137537258828515
-60.240952825940249
-50.360889483595106
-40.339051373346426
-30.307483344687382
-20.340994323029306
-10.395179275812406
00.487775892290028
10.479774929789653
20.345939882924494
30.243516750262989
40.311251723654423
50.348366433385266
60.396887473842276
70.525568053963978
80.477720202250854
90.372432566186509
100.423845592215471
110.397144244179591
120.393116711114787
130.399650329530564
140.304131784222107


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
par6 <- as.numeric(par6)
par7 <- as.numeric(par7)
if (par1 == 0) {
x <- log(x)
} else {
x <- (x ^ par1 - 1) / par1
}
if (par5 == 0) {
y <- log(y)
} else {
y <- (y ^ par5 - 1) / par5
}
if (par2 > 0) x <- diff(x,lag=1,difference=par2)
if (par6 > 0) y <- diff(y,lag=1,difference=par6)
if (par3 > 0) x <- diff(x,lag=par4,difference=par3)
if (par7 > 0) y <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',ylab='CCF',xlab='Lag (k)'))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Cross Correlation Function',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of X series',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of X series',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of X series',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal Period (s)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of Y series',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of Y series',header=TRUE)
a<-table.element(a,par6)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of Y series',header=TRUE)
a<-table.element(a,par7)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'k',header=TRUE)
a<-table.element(a,'rho(Y[t],X[t+k])',header=TRUE)
a<-table.row.end(a)
mylength <- length(r$acf)
myhalf <- floor((mylength-1)/2)
for (i in 1:mylength) {
a<-table.row.start(a)
a<-table.element(a,i-myhalf-1,header=TRUE)
a<-table.element(a,r$acf[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')